Triple
T24474116
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Terengganu Sultanate |
E617181
|
entity |
| Predicate | hasLegalInfluenceFrom |
P141840
|
FINISHED |
| Object | Islamic law |
—
|
NE NERFINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Islamic law | Statement: [Terengganu Sultanate, hasLegalInfluenceFrom, Islamic law]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasLegalInfluenceFrom Context triple: [Terengganu Sultanate, hasLegalInfluenceFrom, Islamic law]
-
A.
hasCommonLegalInfluence
chosen
Indicates that two or more entities are subject to, shaped by, or governed under the same legal authority, framework, or precedent.
-
B.
influencedByCourtCase
Indicates that an entity’s state, decision, or development is shaped or altered as a result of a specific court case or its outcome.
-
C.
hasLegalAuthorityFrom
Indicates that one entity possesses formal legal power, rights, or authorization that originates from or is granted by another entity.
-
D.
hasLoanInfluenceFrom
Indicates that one entity’s loan terms, availability, or conditions are affected or shaped by another entity.
-
E.
influencedCourtDecision
Indicates that one entity had an effect on or contributed to the outcome of a court’s decision regarding another entity or matter.
- F. None of above.
Provenance (3 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69e2d7f197588190889a03e620558059 |
completed | April 18, 2026, 1:01 a.m. |
| NER | Named-entity recognition | batch_69f299457ce081909e8d95fd482928dc |
completed | April 29, 2026, 11:50 p.m. |
| PD | Predicate disambiguation | batch_69f287d76c7c81909494f12e606a9149 |
completed | April 29, 2026, 10:36 p.m. |
Created at: April 18, 2026, 2:20 a.m.